[1]} propose a meta-architecture for multi-task learning. They use part-of-speech tagging—a fundamental syntactic task—as the auxiliary task, and observe that chunking, NER, and semantic role labelling tasks can benefit from the auxiliary task, outperforming the single task learning baseline. Similar ideas have also been explored by [2]}, [3]}. For example, [3]} use low level NLP task, such as part-of-speech tagging, to improve the higher level tasks, such as chunking and CCG Supertagging. They design specialised multi level LSTM networks that have part-of-speech supervision at the innermost layer, and other tasks the outermost layer.
